Normal Multivariate |
The multivariate normal distribution or multivariate Gaussian distribution, is a generalization of the one-dimensional (univariate) normal distribution to higher dimensions. A random vector is said to be multivariate normally distributed if every linear combination of its components has a univariate normal distribution.
The multivariate normal distribution is defined by the parameters k-dimensional mean vector μ and k x k covariance matrix Σ.
This topic contains the following sections:
ConstructorsConstructor | Description | Performance |
|---|---|---|
set μ and Σ | Creates new instance of normal multivariate with user specified parameters. | |
set μ, Σ and random generator | Creates new instance of Dirichlet with user specified parameters. | |
default settings | Creates new instance of normal distribution with default settings (0, In). |
MethodsMethod | Description | Performance |
|---|---|---|
sample | Generates random variable sample.
Generates series of random variable samples. |
PropertiesProperty | Description | Performance | ||
|---|---|---|---|---|
is covariance positive definite | Indicates whether user specified covariance matrix is positive definite.
| |||
covariance | Covariance matrix of multivariate distribution. | |||
method of generation | Method of normal distribution generation. See Normal |
Note